evidence {AICcmodavg} | R Documentation |
This function compares two models of a candidate model set based on their evidence ratio (i.e., ratio of Akaike weights). The default computes the evidence ratio of the Akaike weights between the top-ranked model and a lower-ranked model. You must supply a model selection table of class 'aictab' as the first argument.
evidence(aic.table, model.high = "top", model.low)
aic.table |
a model selection table of class 'aictab' such as that produced by 'aictab'. |
model.high |
the top-ranked model (default), or alternatively, the name of another model as it appears in the model selection table. |
model.low |
the name of a lower-ranked model such as it appears in the model selection table. |
The default compares the Akaike weights of the top-ranked model to another model of the candidate model set. The evidence ratio can be interpreted as the number of times a given model is more parsimonious than a lower-ranked model. If one desires an evidence ratio that does not involve a comparison with the top-ranking model, the name of the required model must be specified in the model.high argument.
'evidence' produces an object of class 'evidence' with the following components:
Model.high |
the top-ranked model among the two compared. |
Model.low |
the lower-ranked model among the two compared. |
Ev.ratio |
the evidence ratio between the two models compared. |
Marc J. Mazerolle
Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.
AICc
, aictab
, c_hat
,
modavg
, importance
, confset
,
modavgpred
##run example from Burnham and Anderson (2002, p. 183) with two ##non-nested models data(pine) Cand.set <- list( ) Cand.set[[1]] <- lm(y ~ x, data = pine) Cand.set[[2]] <- lm(y ~ z, data = pine) ##assign model names Modnames <- c("raw density", "density corrected for resin content") ##compute model selection table aicctable.out <- aictab(cand.set = Cand.set, modnames = Modnames) ##compute evidence ratio evidence(aic.table = aicctable.out, model.low = "raw density") ##round to 4 digits after decimal point print(evidence(aic.table = aicctable.out, model.low = "raw density"), digits = 4) ##run models for the Orthodont data set in nlme require(nlme) ##set up candidate model list Cand.models <- list() Cand.models[[1]] <- lme(distance ~ age, data = Orthodont, method = "ML") ##random is ~ age | Subject Cand.models[[2]] <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1, method = "ML") Cand.models[[3]] <- lme(distance ~ 1, data = Orthodont, random = ~ 1, method = "ML") ##create a vector of model names Modnames <- NULL for (i in 1:length(Cand.models)) { Modnames[i] <- paste("mod", i, sep = "") } ##compute AICc table aic.table.1 <- aictab(cand.set = Cand.models, modnames = Modnames, second.ord = TRUE) ##compute evidence ratio between best model and second-ranked model evidence(aic.table = aic.table.1, model.high = "top", model.low = "mod1") ##compute evidence ratio between second-best model and third-ranked model evidence(aic.table = aic.table.1, model.high = "mod1", model.low = "mod3")